Data

The quality of light by which we scrutinize our lives has direct bearing upon the product which we live, and upon the changes which we hope to bring about through those lives. – Audre Lorde

With education being a central component in career opportunity and access, the investigation of the economics associated with graduation are worth investigating. FiveThirtyEight published data from American Community Survey 2010-2012 Public Use Microdata Series related to 173 college majors, broken down by graduation rates, income, gender, and general labor force information. The type of job graduates ended up with with corresponding employment and income information are included.

This dashboard examines metrics specifically related to major, gender, and employment rates in an exploratory capacity. This exploration, like many others, hopes to shed light and push critical thinking as it relates to higher education and labor market systems and structures – at least informally, in day-to-day settings. Contributions and critiques are welcome.

References

Final Plot

What’s the story?

The graph above represents the median income for each major by share of women, grouped by major category. With the original dataset dividing majors into 16 categories, certain categories were combined and compressed in 10 categories more resembling those used by CollegeBoard. The 173 majors were maintained and plotted, grouped by color by the major categories. Median income for each major is shown on the y-axis, and the confidence interval shows a roughly negative linear relationship Additionally, the share of women variable was calculated using the number of women and total number of individuals in each major category (note: this dataset coded gender as a binary).

The confidence interval added to the graph above shows a roughly negative and linear relationship between the share of women in a particular major and what that major ends up making (median income). For example, petroleum engineers claim the highest median income of $110,000 while being comprised of only 12% women. In contrast early childhood education is 97% women and makes a median salary of $28,000.

Evolution of the Final product

I started this visual interested in examining what the share of women are for the majors included in the dataset. I started by faceting by major category, but this visual made it difficult to compare across groups or identify any larger-scale trends. Also, I wanted to add color, but coloring by the major category potentially made the visual misleading.

From there, I wondered how the majors would look together on the same graph, grouped by the major category. With there being 16 unique categories in the dataset originally, there were too many distinct variables to graph in a way that is receptive to the viewer (there simply were not 16 distinct values to encode). However, I did feel that this second iteration got me closer to the graph I wanted to eventually produce, as I could see what was potentially a moderate negative relationship between share of women and median income when grouped by major.

Using fct_collapse, major categories were redone, with help from CollegeBoard guidelines, and geom_smooth was added to explore the the relationship between x and y. Since I still wanted the focus of the graph to be each of the majors/major groups rather than the strict line, I removed the estimated line and kept the confidence interval as more of a background and guide to the relationship seen by eye. I finally used geom_label to clarify the interpretation of the graph by providing some examples.

Final Plot

What’s the story?

Here is what I did.

Evolution of the Final product

Here is what I did.

Final Plot

What’s the story?

Here is what I did.

Evolution of the Final product

Here is what I did.